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AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

Chemistry

AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

J. Lan, A. Palizhati, et al.

Discover AdsorbML, a groundbreaking machine learning algorithm developed by Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, and others, which drastically enhances the speed and accuracy of calculating adsorption energies for adsorbate-catalyst interactions. With a remarkable 87.36% success rate and a speed 2000 times faster than traditional methods, this research is set to revolutionize the field.

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Playback language: English
Abstract
This paper introduces AdsorbML, a machine learning algorithm that significantly accelerates the computation of adsorption energies for adsorbate-catalyst surface interactions. The algorithm offers a range of accuracy-efficiency trade-offs, with one balanced option achieving an 87.36% success rate while being ~2000 times faster than traditional methods. To facilitate benchmarking, the authors introduce the Open Catalyst Dense dataset, containing nearly 1000 diverse surfaces and ~100,000 unique configurations.
Publisher
npj Computational Materials
Published On
Sep 22, 2023
Authors
Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi
Tags
AdsorbML
machine learning
adsorption energies
catalyst
Open Catalyst Dense dataset
efficiency
accuracy

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